tf.train.LoggingTensorHook

class tf.train.LoggingTensorHook

See the guide: Training > Training Hooks

Prints the given tensors once every N local steps or once every N seconds.

The tensors will be printed to the log, with INFO severity.

Methods

__init__(tensors, every_n_iter=None, every_n_secs=None)

Initializes a LoggingHook monitor.

Args:

• tensors: dict that maps string-valued tags to tensors/tensor names, or iterable of tensors/tensor names.
• every_n_iter: int, print the values of tensors once every N local steps taken on the current worker.
• every_n_secs: int or float, print the values of tensors once every N seconds. Exactly one of every_n_iter and every_n_secs should be provided.

Raises:

• ValueError: if every_n_iter is non-positive.

after_create_session(session, coord)

Called when new TensorFlow session is created.

This is called to signal the hooks that a new session has been created. This has two essential differences with the situation in which begin is called:

• When this is called, the graph is finalized and ops can no longer be added to the graph.
• This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.

Args:

• session: A TensorFlow Session that has been created.
• coord: A Coordinator object which keeps track of all threads.

after_run(run_context, run_values)

before_run(run_context)

begin()

end(session)

Called at the end of session.

The session argument can be used in case the hook wants to run final ops, such as saving a last checkpoint.

Args:

• session: A TensorFlow Session that will be soon closed.

Defined in tensorflow/python/training/basic_session_run_hooks.py.